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COVID-19 underreporting and its impact on vaccination strategies
BACKGROUND: Underreporting cases of infectious diseases poses a major challenge in the analysis of their epidemiological characteristics and dynamical aspects. Without accurate numerical estimates it is difficult to precisely quantify the proportions of severe and critical cases, as well as the mort...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552982/ https://www.ncbi.nlm.nih.gov/pubmed/34711190 http://dx.doi.org/10.1186/s12879-021-06780-7 |
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author | Albani, Vinicius Loria, Jennifer Massad, Eduardo Zubelli, Jorge |
author_facet | Albani, Vinicius Loria, Jennifer Massad, Eduardo Zubelli, Jorge |
author_sort | Albani, Vinicius |
collection | PubMed |
description | BACKGROUND: Underreporting cases of infectious diseases poses a major challenge in the analysis of their epidemiological characteristics and dynamical aspects. Without accurate numerical estimates it is difficult to precisely quantify the proportions of severe and critical cases, as well as the mortality rate. Such estimates can be provided for instance by testing the presence of the virus. However, during an ongoing epidemic, such tests’ implementation is a daunting task. This work addresses this issue by presenting a methodology to estimate underreported infections based on approximations of the stable rates of hospitalization and death. METHODS: We present a novel methodology for the stable rate estimation of hospitalization and death related to the Corona Virus Disease 2019 (COVID-19) using publicly available reports from various distinct communities. These rates are then used to estimate underreported infections on the corresponding areas by making use of reported daily hospitalizations and deaths. The impact of underreporting infections on vaccination strategies is estimated under different disease-transmission scenarios using a Susceptible-Exposed-Infective-Removed-like (SEIR) epidemiological model. RESULTS: For the considered locations, during the period of study, the estimations suggest that the number of infected individuals could reach 30% of the population of these places, representing, in some cases, more than six times the observed numbers. These results are in close agreement with estimates from independent seroprevalence studies, thus providing a strong validation of the proposed methodology. Moreover, the presence of large numbers of underreported infections can reduce the perceived impact of vaccination strategies in reducing rates of mortality and hospitalization. CONCLUSIONS: pBy using the proposed methodology and employing a judiciously chosen data analysis implementation, we estimate COVID-19 underreporting from publicly available data. This leads to a powerful way of quantifying underreporting impact on the efficacy of vaccination strategies. As a byproduct, we evaluate the impact of underreporting in the designing of vaccination strategies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06780-7. |
format | Online Article Text |
id | pubmed-8552982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85529822021-10-29 COVID-19 underreporting and its impact on vaccination strategies Albani, Vinicius Loria, Jennifer Massad, Eduardo Zubelli, Jorge BMC Infect Dis Research BACKGROUND: Underreporting cases of infectious diseases poses a major challenge in the analysis of their epidemiological characteristics and dynamical aspects. Without accurate numerical estimates it is difficult to precisely quantify the proportions of severe and critical cases, as well as the mortality rate. Such estimates can be provided for instance by testing the presence of the virus. However, during an ongoing epidemic, such tests’ implementation is a daunting task. This work addresses this issue by presenting a methodology to estimate underreported infections based on approximations of the stable rates of hospitalization and death. METHODS: We present a novel methodology for the stable rate estimation of hospitalization and death related to the Corona Virus Disease 2019 (COVID-19) using publicly available reports from various distinct communities. These rates are then used to estimate underreported infections on the corresponding areas by making use of reported daily hospitalizations and deaths. The impact of underreporting infections on vaccination strategies is estimated under different disease-transmission scenarios using a Susceptible-Exposed-Infective-Removed-like (SEIR) epidemiological model. RESULTS: For the considered locations, during the period of study, the estimations suggest that the number of infected individuals could reach 30% of the population of these places, representing, in some cases, more than six times the observed numbers. These results are in close agreement with estimates from independent seroprevalence studies, thus providing a strong validation of the proposed methodology. Moreover, the presence of large numbers of underreported infections can reduce the perceived impact of vaccination strategies in reducing rates of mortality and hospitalization. CONCLUSIONS: pBy using the proposed methodology and employing a judiciously chosen data analysis implementation, we estimate COVID-19 underreporting from publicly available data. This leads to a powerful way of quantifying underreporting impact on the efficacy of vaccination strategies. As a byproduct, we evaluate the impact of underreporting in the designing of vaccination strategies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06780-7. BioMed Central 2021-10-28 /pmc/articles/PMC8552982/ /pubmed/34711190 http://dx.doi.org/10.1186/s12879-021-06780-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Albani, Vinicius Loria, Jennifer Massad, Eduardo Zubelli, Jorge COVID-19 underreporting and its impact on vaccination strategies |
title | COVID-19 underreporting and its impact on vaccination strategies |
title_full | COVID-19 underreporting and its impact on vaccination strategies |
title_fullStr | COVID-19 underreporting and its impact on vaccination strategies |
title_full_unstemmed | COVID-19 underreporting and its impact on vaccination strategies |
title_short | COVID-19 underreporting and its impact on vaccination strategies |
title_sort | covid-19 underreporting and its impact on vaccination strategies |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552982/ https://www.ncbi.nlm.nih.gov/pubmed/34711190 http://dx.doi.org/10.1186/s12879-021-06780-7 |
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